Become an AI-native designer.
In three weeks.

You start by unlearning the reflex to prompt first and think later. We set the ground rules for the three weeks: AI is leverage, not a replacement for judgment. You leave day one with a clear model of where AI helps, where it hurts, and where your taste is the only thing that decides.

AI collapses the old cost of fidelity: a rough idea and a polished one now take the same five minutes to generate. That's dangerous if you don't know how to use it. You'll practice compressing research and concepting into a single afternoon, and build the habit of staying skeptical of anything AI hands you too fast. You also set up your agent properly: rules, skills, and project context files, so it works like a trained teammate instead of a chat window.

Frame the problem before you touch a tool. Build the fastest real version. Judge it against a real bar, not a vibe. Ship it like it has to survive contact with a stakeholder. This is the loop you'll run on every AI build for the rest of your career. We drill it until it's automatic.

You'll take a real idea from your own work and turn it into a working, interactive build. Not a static mock, not a slide. Using AI prototyping tools directly, the same class of tools product teams ship with, so what you build here is a habit you keep after the cohort ends. You wire in your own design system as you go: design tokens, components, and Figma MCP, so the prototype looks like your product, not a template.

This is the project that anchors the whole cohort. You scope a real feature (something from your actual job, not a made-up brief) and start building it live, with both instructors reviewing your direction every week. By the end of week two it's a working prototype, not a plan. And because your capstone is an AI feature, you work the design-for-AI patterns directly: trust, confidence states, and what good AI UX actually looks like.

AI is confident even when it's wrong. That's the trap. You'll learn to run a real eval against your own output: a repeatable way to tell "looks done" from "is done," so you stop shipping work that only survives a demo.

The gap between a working prototype and something you'd actually ship is where most AI-assisted work falls apart. You'll close that gap on your own capstone, personally reviewed line by line, until it's not a demo anymore: it's a real, production-ready artifact, taken through version control and a live deploy, the same path a production feature travels.

You present your capstone to the full cohort and defend the calls you made: why this framing, why this tradeoff, why this is done. This is the room your work has to survive after InspiringX: stakeholders who ask "why," not just "is it pretty." You leave knowing you can hold your own in it.

The certificate is issued the day your capstone ships. Not for attending: for shipping. Signed by both instructors, carrying a verified credential ID, ready to share on LinkedIn.